Realtime Power Management of a Multi-Source HEV Using Adaptive Dynamic Programing and Probabilistic Drive State Model

Power management in multi-source hybrid electric vehicles (HEVs) is a nontrivial problem dealing with different forms of energy. Optimal-based approaches are not facile to apply in realtime due to their high computational requirements. Rule-based (RB) algorithms are suitable for realtime control; however, the solution provided is non-optimal. Development of applicable optimal-based solution in realtime control can ensure higher efficiency of HEVs. This paper presents a new method for realtime optimal control of multisource HEVs using adaptive dynamic programming (ADP). The developed concept is based on drive state recognition in terms of physics-based parameters. Vehicle operating conditions are offline optimized for each state using NSGA-II optimization tool. The optimized solution can be applied state-wise in realtime using adaptive RB method. To apply ADP, probabilistic drive state model is developed to provide a lookahead window and generate state transition network for the specified horizon. The algorithm is customized in terms of prediction stepsize/length to solve the shortest path problem in realtime. Experimental application is conducted using emulation test-rig to validate the results. Both simulation and experimental results show reduction of total cost function in terms of fuel consumption and on-board charge sustaining.